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Speaker Verification Using Autoregressive Spectrum of Speech Signal in Composite Vector Stochastic Processes Model Representation
Authors: Chmelařová Natalija | Tykhonov Vyacheslav A. | Bezruk Valerij M. | Chmelař Pavel | Rejfek Luboš
Year: 2019
Type of publication: článek v odborném periodiku
Name of source: Journal of Mechanics of Continua and Mathematical Sciences
Publisher name: Institute of Mechanics of Continua and Mathematical Sciences
Place: Kalkata
Page from-to: 178-190
Titles:
Language Name Abstract Keywords
cze Speaker Verification Using Autoregressive Spectrum of Speech Signal in Composite Vector Stochastic Processes Model Representation This paper deals with the speaker verification system similar to a fingerprint or an eye scanner. For these purpose a long-term words' model and its spectral characteristics were used. The speaker verification method uses the word's sound parametric spectrum factorization in composite vector stochastic process representation based on the multiplicative autoregressive model. The developed method enables to receive the words' features with stable characteristics for the same speaker and differ for different speakers. During the training phase speaker's etalon frequencies has to be estimated for a pronounced word repeated several times. In the verification phase a speaker pronouncing the same word, word's frequencies are estimated and compared with the etalon frequencies database to find the best match or his deny. The results presented in the paper showed the high correct identification probability. Composite Vector Stochastic Processes Autoregressive Models; Power Spectrum Density; Speaker Verification
eng Speaker Verification Using Autoregressive Spectrum of Speech Signal in Composite Vector Stochastic Processes Model Representation This paper deals with the speaker verification system similar to a fingerprint or an eye scanner. For these purpose a long-term words' model and its spectral characteristics were used. The speaker verification method uses the word's sound parametric spectrum factorization in composite vector stochastic process representation based on the multiplicative autoregressive model. The developed method enables to receive the words' features with stable characteristics for the same speaker and differ for different speakers. During the training phase speaker's etalon frequencies has to be estimated for a pronounced word repeated several times. In the verification phase a speaker pronouncing the same word, word's frequencies are estimated and compared with the etalon frequencies database to find the best match or his deny. The results presented in the paper showed the high correct identification probability. Composite Vector Stochastic Processes Autoregressive Models; Power Spectrum Density; Speaker Verification

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